Updated: 2020-08-24 06:34:47 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
Massachusetts 1.38 124236 377
South Dakota 1.30 11004 159
North Dakota 1.28 9795 212
Maine 1.21 4341 28
Iowa 1.19 56773 707
North Carolina 1.13 155248 1582
Wyoming 1.13 3591 48
Mississippi 1.12 77927 876
Oklahoma 1.11 53115 766
Illinois 1.10 220900 2107
Minnesota 1.08 69348 683
Montana 1.07 6396 114
Missouri 1.04 67507 1096
Utah 1.04 49214 379
Arkansas 1.03 55908 613
Colorado 1.03 55287 326
Kentucky 1.02 45754 682
New Mexico 1.02 24342 146
Tennessee 1.02 140918 1580
Indiana 1.01 88055 877
South Carolina 1.01 111970 829
Virginia 1.01 89491 714
West Virginia 1.01 9268 118
Wisconsin 1.01 70769 734
Alabama 0.99 115355 981
Michigan 0.99 106230 669
New York 0.99 434482 632
Ohio 0.99 114990 985
Georgia 0.98 235387 2618
Kansas 0.98 37937 463
Nebraska 0.98 31878 244
Oregon 0.98 24959 259
Nevada 0.96 65809 655
Maryland 0.95 104634 573
Pennsylvania 0.95 133795 686
Washington 0.95 73928 581
Texas 0.91 605705 6031
Vermont 0.91 1544 6
Arizona 0.90 198433 701
New Jersey 0.90 191084 322
Rhode Island 0.90 19236 82
Idaho 0.88 30129 318
Louisiana 0.88 142660 705
Connecticut 0.87 51392 72
Florida 0.87 601040 4153
New Hampshire 0.87 7105 17
California 0.86 672862 6345
Delaware 0.74 16664 68

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 26 1 2.3 178 370 11
Grays Harbor WA 24 2 1.7 195 270 11
King WA 1 3 1.0 18742 870 150
Kitsap WA 16 4 1.3 927 350 19
Pierce WA 3 5 1.0 7197 840 68
Grant WA 9 6 0.9 2144 2260 41
Benton WA 6 7 1.1 4161 2140 20
Snohomish WA 4 8 0.9 6794 860 35
Yakima WA 2 9 0.9 11491 4610 34
Spokane WA 5 13 0.8 5024 1010 34
Franklin WA 7 19 0.8 3983 4390 20
Clark WA 8 20 0.8 2414 520 19
OR
county ST case rank severity R_e cases cases/100k daily cases
Marion OR 3 1 1.1 3519 1050 49
Jackson OR 7 2 1.2 691 320 20
Multnomah OR 1 3 0.9 5659 710 50
Malheur OR 6 4 1.1 1031 3390 19
Washington OR 2 5 1.0 3544 610 32
Clackamas OR 5 6 1.0 1812 450 20
Yamhill OR 10 7 1.1 586 560 11
Umatilla OR 4 10 0.7 2550 3320 14
Deschutes OR 8 14 0.9 670 370 4
Lane OR 9 18 0.9 656 180 4
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 0.9 232052 2300 1480
Fresno CA 7 2 1.0 23358 2390 450
San Bernardino CA 4 3 0.9 45291 2120 623
San Diego CA 5 4 1.0 36563 1110 272
Alameda CA 8 5 0.9 16923 1030 258
Orange CA 3 6 0.9 46083 1460 355
Sonoma CA 24 7 1.1 5174 1030 121
Riverside CA 2 9 0.7 50286 2110 436
Kern CA 6 13 0.8 28203 3190 233
San Joaquin CA 9 18 0.7 16144 2200 153

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.9 131938 3100 378
Pinal AZ 4 2 1.1 9216 2200 65
Pima AZ 2 3 0.8 20574 2020 137
Graham AZ 13 4 1.2 668 1760 11
Gila AZ 12 5 1.1 1076 2010 12
Yuma AZ 3 6 0.9 12071 5810 30
Mohave AZ 6 7 0.9 3499 1700 19
Apache AZ 7 8 1.0 3304 4620 9
Santa Cruz AZ 9 9 1.1 2738 5880 5
Coconino AZ 8 11 0.9 3244 2310 10
Navajo AZ 5 12 0.8 5503 5060 7
CO
county ST case rank severity R_e cases cases/100k daily cases
Arapahoe CO 2 1 1.2 7879 1240 50
Douglas CO 8 2 1.3 1945 590 21
Adams CO 3 3 1.1 7193 1450 57
Denver CO 1 4 1.0 10890 1570 48
Boulder CO 7 5 1.1 2222 690 12
El Paso CO 4 6 0.9 5807 840 41
Jefferson CO 5 7 0.9 4588 800 26
Larimer CO 9 8 1.0 1804 530 18
Weld CO 6 9 1.0 3934 1330 16
UT
county ST case rank severity R_e cases cases/100k daily cases
Utah UT 2 1 1.1 10134 1720 110
Sanpete UT 13 2 1.6 160 540 4
Salt Lake UT 1 3 1.0 22822 2040 146
Summit UT 7 4 1.4 811 2000 11
Juab UT 15 5 1.6 102 930 4
Davis UT 3 6 1.1 3606 1060 32
Cache UT 6 7 1.3 2041 1670 11
Weber UT 4 9 1.1 3105 1250 24
Washington UT 5 11 1.0 2714 1690 16
Tooele UT 9 13 0.9 643 990 4
San Juan UT 8 17 1.0 661 4330 1
NM
county ST case rank severity R_e cases cases/100k daily cases
Socorro NM 21 1 2.2 76 450 0
Bernalillo NM 1 2 1.1 5574 820 36
Santa Fe NM 8 3 1.3 770 520 12
Rio Arriba NM 14 4 1.4 345 880 3
Chaves NM 11 5 1.1 620 950 13
Grant NM 22 6 1.6 76 270 1
Lea NM 7 7 1.0 1043 1490 18
Sandoval NM 5 9 1.2 1188 840 5
San Juan NM 3 12 1.0 3140 2460 7
Doña Ana NM 4 15 0.7 2729 1270 12
McKinley NM 2 16 0.8 4156 5700 6
Cibola NM 9 17 1.0 722 2680 3
Otero NM 6 18 1.0 1114 1690 1

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Hunterdon NJ 19 1 1.5 1246 1000 5
Ocean NJ 7 2 1.1 10989 1860 26
Middlesex NJ 4 3 1.0 18478 2240 26
Passaic NJ 5 4 0.9 18321 3630 35
Cape May NJ 21 5 1.3 882 940 4
Essex NJ 2 6 1.0 20349 2560 26
Burlington NJ 12 7 1.0 6319 1420 19
Camden NJ 9 9 0.9 9025 1780 25
Hudson NJ 3 11 0.9 20210 3020 22
Monmouth NJ 8 14 0.8 10686 1710 16
Union NJ 6 15 0.8 17149 3100 16
Bergen NJ 1 17 0.5 21609 2320 22
PA
county ST case rank severity R_e cases cases/100k daily cases
Susquehanna PA 41 1 1.6 246 600 4
Philadelphia PA 1 2 0.9 33000 2090 106
Montgomery PA 2 3 1.1 10707 1300 46
Allegheny PA 4 4 1.0 9896 810 70
Jefferson PA 56 5 1.6 89 200 2
Berks PA 7 6 1.1 5810 1390 33
Westmoreland PA 15 7 1.2 1727 490 17
Lancaster PA 6 8 1.0 6461 1200 39
Delaware PA 3 11 0.9 10076 1790 50
Bucks PA 5 15 0.9 7588 1210 25
Chester PA 8 29 0.8 5464 1060 17
Lehigh PA 9 38 0.8 5130 1410 9
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.1 14704 1780 113
Prince George’s MD 1 2 0.9 25851 2850 108
Anne Arundel MD 5 3 1.0 7968 1400 45
Montgomery MD 2 4 0.9 19509 1880 68
Baltimore city MD 4 5 0.9 14006 2280 82
Harford MD 8 6 1.0 2312 920 26
Washington MD 12 7 1.1 1228 820 17
Frederick MD 7 9 1.0 3353 1350 20
Howard MD 6 11 0.9 4226 1340 22
Charles MD 9 14 0.8 2269 1440 14
VA
county ST case rank severity R_e cases cases/100k daily cases
Tazewell VA 67 1 1.7 166 390 6
Fauquier VA 22 2 1.6 694 1000 10
Fairfax VA 1 3 1.1 17625 1540 96
Prince William VA 2 4 1.1 10327 2260 68
Smyth VA 54 5 1.5 217 700 8
Pulaski VA 76 6 1.7 106 310 2
Montgomery VA 36 7 1.4 367 370 7
Newport News city VA 9 10 1.1 2126 1180 23
Chesterfield VA 5 11 1.0 4850 1430 34
Arlington VA 8 15 1.0 3382 1460 24
Loudoun VA 3 16 1.0 5711 1480 31
Virginia Beach city VA 4 17 0.9 5710 1270 45
Henrico VA 6 19 1.0 4324 1330 31
Norfolk city VA 7 22 0.9 4182 1700 29
WV
county ST case rank severity R_e cases cases/100k daily cases
Marshall WV 19 1 2.0 135 430 1
Jackson WV 18 2 1.7 188 650 4
Monongalia WV 2 3 1.3 1048 1000 10
Taylor WV 26 4 1.4 101 600 5
Kanawha WV 1 5 1.0 1217 660 21
Logan WV 5 6 0.9 452 1340 16
Jefferson WV 7 7 1.3 320 570 2
Berkeley WV 3 12 0.9 768 680 5
Cabell WV 4 14 0.8 495 520 5
Raleigh WV 6 16 0.7 327 430 4
Ohio WV 9 21 0.8 288 680 1
Wood WV 8 24 0.6 294 340 2
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.0 7827 1410 43
Kent DE 3 2 0.6 2562 1470 12
Sussex DE 2 3 0.5 6275 2860 14

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Houston AL 16 1 1.5 1772 1700 35
Geneva AL 59 2 1.7 369 1390 12
Tuscaloosa AL 5 3 1.2 5009 2430 59
Dale AL 33 4 1.4 979 1990 13
Cleburne AL 66 5 1.5 186 1250 7
Etowah AL 11 6 1.2 2608 2530 37
Marshall AL 8 7 1.3 3524 3700 27
Jefferson AL 1 13 0.9 15062 2280 102
Lee AL 9 15 1.0 3267 2050 30
Mobile AL 2 18 0.8 11694 2820 76
Montgomery AL 3 23 0.9 7602 3350 43
Baldwin AL 6 28 0.9 4144 1990 31
Madison AL 4 31 0.9 6065 1700 34
Shelby AL 7 32 0.9 3929 1860 27
MS
county ST case rank severity R_e cases cases/100k daily cases
Issaquena MS 82 1 3.1 92 6930 17
Leflore MS 18 2 1.4 1215 4080 32
Madison MS 4 3 1.4 2777 2680 35
Rankin MS 6 4 1.3 2646 1750 37
DeSoto MS 2 5 1.2 4282 2430 55
Jackson MS 5 6 1.2 2756 1940 42
Hinds MS 1 7 1.2 6260 2590 53
Lee MS 9 10 1.0 1995 2350 41
Harrison MS 3 12 1.0 3045 1500 39
Forrest MS 8 23 1.1 2020 2670 17
Jones MS 7 40 1.0 2069 3020 12
LA
county ST case rank severity R_e cases cases/100k daily cases
East Feliciana LA 42 1 1.4 719 3690 14
Rapides LA 10 2 1.1 3678 2800 32
East Baton Rouge LA 2 3 0.9 13344 3000 74
Calcasieu LA 5 4 1.0 7280 3640 34
Vernon LA 36 5 1.3 884 1730 11
Caddo LA 6 6 1.0 7156 2880 33
Ouachita LA 8 7 1.0 5364 3440 34
Jefferson LA 1 9 0.8 16104 3700 46
St. Tammany LA 7 10 0.9 5821 2310 37
Tangipahoa LA 9 11 0.9 3947 3020 31
Orleans LA 3 14 0.9 11185 2870 28
Lafayette LA 4 21 0.7 8147 3390 24

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Miami-Dade FL 1 1 0.8 152504 5620 1036
Broward FL 2 2 0.9 69332 3630 414
Hillsborough FL 4 3 1.0 35754 2590 224
Jefferson FL 64 4 1.8 488 3460 3
Nassau FL 44 5 1.3 1480 1840 25
Orange FL 5 6 0.9 34727 2630 197
Palm Beach FL 3 7 0.9 40604 2810 219
Pinellas FL 7 9 1.0 19444 2030 108
Duval FL 6 10 0.9 25577 2770 139
Polk FL 9 12 0.9 16274 2430 126
Lee FL 8 14 0.9 18026 2510 97
GA
county ST case rank severity R_e cases cases/100k daily cases
Clinch GA 125 1 1.8 256 3800 8
Fulton GA 1 2 1.0 23822 2330 261
Chattahoochee GA 49 3 1.5 926 8600 20
Lumpkin GA 75 4 1.5 562 1760 25
Gwinnett GA 2 5 1.0 23171 2570 234
Stewart GA 108 6 1.6 318 5260 8
Cobb GA 4 7 0.9 15945 2140 153
Clayton GA 7 8 1.1 6053 2170 80
DeKalb GA 3 9 0.9 15997 2150 137
Hall GA 5 12 1.0 7036 3590 69
Richmond GA 8 16 0.9 5706 2830 89
Chatham GA 6 23 0.9 6686 2330 60
Muscogee GA 9 44 0.8 5299 2690 32

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Maverick TX 33 1 1.7 2969 5120 80
Live Oak TX 121 2 2.0 275 2270 7
Harris TX 1 3 1.0 98512 2140 920
Dallas TX 2 4 0.9 72600 2810 1103
Hidalgo TX 6 5 1.1 24468 2880 362
Montgomery TX 16 6 1.3 8022 1450 135
Bexar TX 3 7 1.1 45186 2350 180
Travis TX 5 9 1.0 25882 2150 223
Cameron TX 8 10 1.0 19238 4560 170
Tarrant TX 4 12 0.8 40120 1990 360
El Paso TX 7 24 0.8 19574 2340 174
Nueces TX 9 33 0.7 18112 5020 144
OK
county ST case rank severity R_e cases cases/100k daily cases
Comanche OK 8 1 1.9 1055 860 34
Kingfisher OK 42 2 1.7 219 1400 12
Haskell OK 45 3 1.7 151 1190 12
Oklahoma OK 1 4 1.1 12598 1610 157
Cleveland OK 3 5 1.3 3533 1280 50
Tulsa OK 2 6 1.1 12338 1920 142
Pottawatomie OK 14 7 1.3 720 1000 28
McCurtain OK 9 10 1.4 947 2870 9
Wagoner OK 7 17 1.0 1057 1360 14
Canadian OK 4 22 1.0 1416 1040 14
Rogers OK 5 32 0.8 1219 1340 13
Texas OK 6 35 1.0 1097 5190 3

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Isabella MI 32 1 2.1 295 420 15
Wayne MI 1 2 1.0 30010 1700 127
Oakland MI 2 3 0.9 17211 1380 112
Macomb MI 3 4 0.9 12227 1410 101
Kent MI 4 5 1.1 8027 1250 38
Wexford MI 53 6 1.6 87 260 2
Saginaw MI 8 7 1.1 2351 1220 27
Genesee MI 5 11 1.1 3853 940 16
Jackson MI 7 22 1.1 2493 1570 5
Washtenaw MI 6 25 0.9 3262 890 12
Ottawa MI 9 26 0.9 2007 710 10
WI
county ST case rank severity R_e cases cases/100k daily cases
Brown WI 4 1 1.4 4916 1890 67
Milwaukee WI 1 2 1.0 23278 2440 155
Clark WI 39 3 1.6 223 650 5
Fond du Lac WI 15 4 1.3 926 910 26
Iron WI 50 5 1.6 108 1890 4
Oconto WI 30 6 1.3 361 960 12
Rock WI 7 7 1.3 1696 1050 12
Outagamie WI 9 8 1.1 1554 840 25
Waukesha WI 2 11 0.9 5263 1320 67
Dane WI 3 13 0.9 5093 960 39
Racine WI 5 14 1.0 3841 1970 24
Kenosha WI 6 22 0.9 2882 1710 14
Walworth WI 8 23 0.9 1605 1560 17

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.1 21635 1750 182
Le Sueur MN 26 2 1.5 316 1130 12
Chisago MN 31 3 1.6 262 480 8
Stearns MN 5 4 1.4 3078 1960 19
Ramsey MN 2 5 1.1 8569 1580 79
Dakota MN 3 6 1.1 5255 1260 67
Sibley MN 49 7 1.6 108 720 3
Washington MN 6 8 1.1 2607 1030 41
Anoka MN 4 10 1.0 4330 1250 48
Scott MN 8 19 0.9 1844 1290 19
Olmsted MN 7 29 0.9 1916 1250 12
Nobles MN 9 32 1.1 1824 8350 5
SD
county ST case rank severity R_e cases cases/100k daily cases
Meade SD 10 1 1.9 157 570 10
Custer SD 20 2 2.0 78 910 7
Pennington SD 2 3 1.4 1030 940 15
Lawrence SD 17 4 1.6 105 420 6
Brown SD 5 5 1.4 543 1400 11
Minnehaha SD 1 6 1.2 4896 2620 42
Beadle SD 4 7 1.7 611 3330 2
Codington SD 7 8 1.4 214 760 9
Lincoln SD 3 9 1.1 789 1440 13
Brookings SD 9 10 1.3 179 520 4
Yankton SD 8 13 1.0 180 790 5
Union SD 6 16 1.1 233 1540 2
ND
county ST case rank severity R_e cases cases/100k daily cases
Grand Forks ND 3 1 1.6 989 1400 39
Ward ND 6 2 1.6 395 570 21
Burleigh ND 2 3 1.3 1705 1820 51
Stark ND 5 4 1.2 532 1720 24
Cass ND 1 5 1.2 3266 1870 22
Stutsman ND 11 6 1.6 137 650 1
Morton ND 4 7 1.1 539 1760 13
Benson ND 8 9 1.2 208 3020 6
Williams ND 7 12 0.9 327 960 4
Mountrail ND 9 17 0.5 161 1590 1

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Fairfield CT 1 1 0.9 18471 1960 32
Tolland CT 7 2 1.3 1086 720 3
Hartford CT 3 3 0.9 13018 1450 15
New Haven CT 2 4 0.8 13460 1570 16
Litchfield CT 4 5 0.8 1647 900 2
New London CT 5 6 0.6 1510 560 3
Windham CT 8 7 0.6 771 660 1
Middlesex CT 6 8 0.6 1428 870 1
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.4 22760 2870 99
Essex MA 3 2 1.4 18466 2360 71
Middlesex MA 1 3 1.4 27088 1700 69
Worcester MA 4 4 1.4 13971 1700 39
Bristol MA 6 5 1.4 9579 1710 23
Hampden MA 8 6 1.4 7804 1660 22
Plymouth MA 7 7 1.3 9457 1850 22
Norfolk MA 5 8 1.3 10857 1550 23
Barnstable MA 9 12 1.3 1817 850 2
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 0.9 16230 2560 68
Washington RI 3 2 1.2 665 530 5
Kent RI 2 3 0.9 1600 980 6
Bristol RI 5 4 1.0 330 670 1
Newport RI 4 5 0.8 412 500 1

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Essex NY 49 1 2.1 106 280 8
New York City NY 1 2 0.9 236856 2810 283
Erie NY 7 3 1.2 9446 1030 48
Nassau NY 3 4 1.1 44246 3260 46
St. Lawrence NY 34 5 1.9 266 240 0
Broome NY 17 6 1.3 1232 630 10
Suffolk NY 2 7 1.0 44501 2990 50
Rockland NY 5 8 1.2 14133 4370 17
Westchester NY 4 11 0.9 36688 3790 32
Dutchess NY 9 16 1.1 4763 1620 12
Orange NY 6 17 1.0 11352 3000 13
Monroe NY 8 21 0.9 5324 720 22

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Windham VT 3 1 1.3 114 260 1
Bennington VT 5 2 1.2 95 260 1
Chittenden VT 1 3 0.8 774 480 2
Rutland VT 4 4 0.8 103 170 0
Addison VT 6 5 0.8 77 210 0
Franklin VT 2 6 0.6 122 250 0
Windsor VT 7 7 0.4 75 140 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 1.5 738 360 8
Kennebec ME 5 2 1.9 178 150 1
Penobscot ME 4 3 1.1 219 140 6
Cumberland ME 1 4 0.9 2164 740 5
Androscoggin ME 3 5 1.0 591 550 2
NH
county ST case rank severity R_e cases cases/100k daily cases
Merrimack NH 3 1 1.3 486 330 2
Cheshire NH 6 2 1.3 113 150 1
Rockingham NH 2 3 1.0 1758 580 5
Hillsborough NH 1 4 0.7 3986 970 7
Grafton NH 7 5 1.2 109 120 0
Carroll NH 8 6 0.9 99 210 0
Strafford NH 4 7 0.8 371 290 1
Belknap NH 5 8 0.2 121 200 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Edgefield SC 41 1 1.8 433 1620 14
Richland SC 3 2 1.0 9938 2430 86
Georgetown SC 17 3 1.3 1659 2690 20
Spartanburg SC 6 4 1.1 4717 1560 48
Anderson SC 12 5 1.1 2886 1470 43
Pickens SC 16 6 1.3 2021 1650 16
Marion SC 34 7 1.4 637 2020 9
Lexington SC 5 8 1.1 5471 1910 39
Charleston SC 1 9 0.9 13317 3370 66
Greenville SC 2 10 1.0 11559 2320 45
Florence SC 9 18 0.9 4015 2900 39
Horry SC 4 20 0.9 9106 2840 34
Beaufort SC 8 21 1.0 4550 2490 30
Berkeley SC 7 29 0.8 4616 2210 23
NC
county ST case rank severity R_e cases cases/100k daily cases
Orange NC 23 1 1.7 1849 1290 70
Wake NC 2 2 1.2 13666 1310 141
Pitt NC 15 3 1.3 2619 1480 60
Cherokee NC 71 4 1.6 367 1330 12
Mecklenburg NC 1 5 1.1 24299 2300 161
Guilford NC 4 6 1.2 6360 1210 64
Scotland NC 58 7 1.4 561 1590 22
Forsyth NC 5 15 1.1 5819 1570 42
Cumberland NC 7 18 1.0 3724 1120 47
Union NC 8 19 1.0 3707 1640 45
Gaston NC 6 23 1.0 3819 1760 38
Durham NC 3 39 1.0 6650 2170 32
Johnston NC 9 43 1.0 3610 1890 22

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Rosebud MT 9 1 1.5 142 1540 12
Yellowstone MT 1 2 1.2 1784 1130 45
Flathead MT 4 3 1.3 475 480 13
Glacier MT 12 4 1.4 107 780 3
Big Horn MT 3 5 0.9 594 4440 11
Lincoln MT 16 6 1.5 80 410 0
Cascade MT 6 7 1.2 198 240 3
Lewis and Clark MT 8 9 0.9 192 290 2
Gallatin MT 2 11 0.7 1033 990 4
Missoula MT 5 12 0.7 407 350 4
Lake MT 7 14 0.9 193 650 1
WY
county ST case rank severity R_e cases cases/100k daily cases
Albany WY 10 1 1.7 127 330 5
Sheridan WY 11 2 1.5 119 400 6
Fremont WY 1 3 1.2 576 1440 7
Natrona WY 6 4 1.3 266 330 3
Teton WY 3 5 1.2 406 1760 3
Carbon WY 7 6 0.9 197 1270 7
Laramie WY 2 7 1.0 543 560 3
Park WY 9 8 1.0 158 540 2
Sweetwater WY 4 9 1.0 292 660 2
Campbell WY 8 11 0.9 162 340 2
Uinta WY 5 12 1.0 284 1380 1
ID
county ST case rank severity R_e cases cases/100k daily cases
Payette ID 10 1 1.4 555 2410 17
Ada ID 1 2 0.9 10646 2390 96
Power ID 25 3 1.4 104 1350 5
Nez Perce ID 17 4 1.3 234 580 8
Canyon ID 2 5 0.8 6811 3210 60
Bonneville ID 5 6 0.9 1593 1420 33
Jefferson ID 13 7 1.1 313 1120 9
Jerome ID 9 10 1.0 565 2410 6
Bannock ID 6 12 0.8 609 720 10
Twin Falls ID 4 13 0.8 1603 1920 11
Kootenai ID 3 19 0.6 2076 1350 10
Cassia ID 8 23 0.8 574 2430 3
Blaine ID 7 24 0.9 596 2710 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Jackson OH 76 1 2.0 124 380 8
Summit OH 6 2 1.3 4119 760 54
Fayette OH 70 3 1.6 170 590 7
Huron OH 41 4 1.6 461 790 7
Franklin OH 1 5 0.9 20580 1610 145
Montgomery OH 5 6 1.1 5017 940 52
Lucas OH 4 7 1.1 6076 1410 55
Cuyahoga OH 2 8 0.9 14969 1190 93
Butler OH 7 10 1.0 3460 910 41
Hamilton OH 3 12 0.9 10554 1300 60
Mahoning OH 9 48 0.8 2764 1200 11
Marion OH 8 78 0.6 2980 4560 2
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.1 121203 2320 787
McLean IL 18 2 1.6 1043 600 48
Champaign IL 13 3 1.6 1970 940 36
Effingham IL 36 4 1.5 391 1140 28
Cumberland IL 66 5 1.7 107 980 7
DuPage IL 3 6 1.1 13783 1480 128
Fayette IL 63 7 1.7 112 520 6
Will IL 5 8 1.1 10744 1560 122
St. Clair IL 6 11 1.1 5366 2040 76
Kane IL 4 13 1.1 10826 2040 78
Winnebago IL 7 15 1.3 4046 1410 26
Madison IL 9 16 1.0 3582 1350 72
Lake IL 2 19 0.9 13893 1970 81
McHenry IL 8 50 0.8 3619 1180 23
IN
county ST case rank severity R_e cases cases/100k daily cases
Knox IN 52 1 1.6 278 740 15
Greene IN 46 2 1.5 332 1030 10
Daviess IN 41 3 1.4 405 1230 15
Marion IN 1 4 1.0 17592 1860 116
St. Joseph IN 5 5 1.1 4239 1570 58
Lake IN 2 6 1.0 8658 1780 74
Vigo IN 16 7 1.1 1040 970 32
Hendricks IN 8 10 1.1 2169 1350 21
Allen IN 4 12 0.9 4588 1240 44
Hamilton IN 6 13 0.9 3475 1100 45
Elkhart IN 3 18 0.9 5434 2670 32
Vanderburgh IN 7 23 0.9 2341 1290 24
Johnson IN 9 30 1.0 1955 1290 11

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Sullivan TN 20 1 1.4 1416 900 44
Hardin TN 49 2 1.5 606 2350 15
Blount TN 13 3 1.2 1746 1360 41
Marshall TN 56 4 1.4 457 1420 17
Hamilton TN 3 5 1.0 7493 2100 100
Gibson TN 24 6 1.3 987 2010 26
Shelby TN 1 7 0.9 26176 2790 162
Davidson TN 2 9 0.9 25290 3700 149
Knox TN 5 12 1.0 6103 1340 86
Sumner TN 7 18 1.1 3874 2160 36
Williamson TN 6 22 1.0 4145 1900 43
Rutherford TN 4 23 0.9 7479 2440 60
Wilson TN 8 37 1.0 2658 2000 25
Bradley TN 9 39 0.9 2336 2230 27
KY
county ST case rank severity R_e cases cases/100k daily cases
Jefferson KY 1 1 1.0 11256 1470 215
Green KY 70 2 1.6 109 990 8
Pulaski KY 20 3 1.4 465 720 12
Oldham KY 11 4 1.4 745 1140 12
Fayette KY 2 5 1.0 4984 1560 74
Warren KY 3 6 1.1 2978 2360 28
Daviess KY 6 7 1.2 917 920 14
Boone KY 5 19 1.1 1222 950 10
Shelby KY 7 29 1.0 869 1860 8
Kenton KY 4 32 0.9 1628 990 13
Hardin KY 8 33 0.9 841 780 12
Christian KY 9 41 0.8 823 1140 10

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Boone MO 7 1 1.4 1946 1100 59
Greene MO 5 2 1.3 2520 870 91
St. Francois MO 17 3 1.3 829 1250 43
St. Louis MO 1 4 0.9 17955 1800 196
Miller MO 47 5 1.5 201 800 8
St. Charles MO 3 6 1.0 5254 1350 81
Jackson MO 4 7 1.0 5048 730 71
Jefferson MO 6 8 1.1 2407 1080 47
Clay MO 9 11 1.1 1254 520 18
St. Louis city MO 2 14 0.9 5914 1900 44
Jasper MO 8 20 1.0 1454 1220 14
AR
county ST case rank severity R_e cases cases/100k daily cases
Stone AR 55 1 1.9 153 1230 13
Dallas AR 64 2 1.9 88 1180 4
Van Buren AR 67 3 1.8 79 470 4
Craighead AR 7 4 1.2 1682 1590 30
Baxter AR 61 5 1.6 111 270 5
Jefferson AR 5 6 1.2 1906 2710 32
Jackson AR 50 7 1.4 175 1020 8
Washington AR 1 9 1.1 6613 2890 27
Pulaski AR 2 10 1.0 6550 1660 67
Pope AR 8 11 1.1 1595 2510 24
Benton AR 3 12 1.1 5087 1960 26
Sebastian AR 4 14 1.0 2692 2110 37
Crittenden AR 9 31 0.9 1577 3220 14
Hot Spring AR 6 49 0.7 1687 5030 8

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1206.3 seconds to compute.
2020-08-24 06:54:53

version history

Today is 2020-08-24.
96 days ago: Multiple states.
88 days ago: \(R_e\) computation.
85 days ago: created color coding for \(R_e\) plots.
80 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
80 days ago: “persistence” time evolution.
73 days ago: “In control” mapping.
73 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
65 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
60 days ago: Added Per Capita US Map.
58 days ago: Deprecated national map.
54 days ago: added state “Hot 10” analysis.
49 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
47 days ago: added per capita disease and mortaility to state-level analysis.
35 days ago: changed to county boundaries on national map for per capita disease.
30 days ago: corrected factor of two error in death trend data.
26 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
21 days ago: added county level “baseline control” and \(R_e\) maps.
17 days ago: fixed normalization error on total disease stats plot.
10 days ago: Corrected some text matching in generating county level plots of \(R_e\).
4 days ago: adapter knot spacing for spline.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.